The present invention relates to a plant control system and in particular or system, to a thermal power plant.
The plant control system processes a measurement signal of an operation state amount obtained from a plant to be controlled, calculates an operation signal to be supplied to the plant to be controlled, and transmits it as a control instruction to the control system.
The plant control system uses an algorithm for calculating the operation signal so that the measurement signal of the plant operation state amount satisfies its target value.
It is possible to use the PI (proportion/integration) control algorithm for controlling the plant. In the PI control, the deviation of the plant measurement signal from its target value is multiplied by the proportion gain. The obtained value is added by the value obtained by time-integrating the deviation so as to obtain the operation signal to be supplied to the object to be controlled.
On the other hand, in order to automatically correct the control algorithm in accordance with a change of the plant operation state and the environment, the plant may be controlled by using the adaptive control and the learning algorithm.
JP-A-2000-35956 discloses a control apparatus using a reinforced learning method using a model as a method for obtaining the operation signal of the control apparatus for controlling the plant by using the learning algorithm.
In the method using the technique of the reinforced learning method, the control apparatus includes a model for predicting the characteristic of the object to be controlled and a learning part for learning the method for generating such a model input that a model output can achieve the target value.
By inputting the model input learned by the learning part to the model, it is possible to obtain the effect that the model output approaches the target value.
The learning type adaptive control corrects the model by using the measurement signal which has measured the plant operation state and performs re-learning by using the corrected model so as to perform online correction/modification of the control algorithm for accurately controlling the plant.
Accordingly, it is necessary to correct the model within a cycle (control cycle) during which the operation signal outputted from the control apparatus is modified for the plant and use the corrected model for the re-learning so as to complete the learning and correct/modify the control algorithm.
The control cycle can be considered as a time from the plant operation completion to the moment when the plant enters a static state, which is normally several minutes or several tens of minutes.
For example, when controlling a complicated plant such as a thermal power plant, the model input dimension is several tens or several hundreds and the number of combinations of the learning model inputs (search space) increases, which in turn requires a long learning time. As a result, it becomes difficult to online-correct/modify the control algorithm required for accurately controlling the plant.
Consequently, in order to complete the learning within the control cycle, it is necessary to correct the model in accordance with increase of the model input dimension and increase the learning speed of re-learning by using the corrected model.
JP-A-7-160661 discloses a technique for increasing the learning speed in a neural network learning by classifying teacher data into a plurality of patterns according to the combinations of the plant measurement information and extracting a learning pattern according to the control result so as to learn only the teacher data on the extracted pattern.